Generally, the way most genes, cells and viruses functions in humans and other organisms is relatively unknown despite the fact that there have been successful genomic sequencing programs and other types of programs. Thus, there is a need for high-throughput screening that enables one to learn and understand gene functions. High-throughput screening makes it possible to review and analyze hundreds of thousands of gene products. The results of these analyses enables one to review the biological processes that takes place within the cell, which is necessary for the analysis of the cells because cell tracking is important to scientific investigation of biological assays.
Typically, one would need to employ a computer in order to track a cell to mark a single cell of a plurality of cells with a dye and track the cell as it moves from one frame to another. This process of tracking the marked cell movement is error-prone and time consuming, because even though the one cell was marked it still may not be distinguishable from the plurality of cells that existed from one frame to another. Also, the dye utilized to mark the cell may hinder the normal cell cycles so one would not obtain a true image of how the cell works as it moves from one frame to another.
There are several different algorithms utilized by a computer for cell tracking, such as a Kalman filter and particle filter. Kalman filter is an efficient recursive filter which estimates the state of a dynamic system from a series of incomplete and noisy measurements. The Kalman filter employs a tracking method based on a second order statistics in order to track the movement of an object, such as a cell from one point to another. Kalman filter employs the tracking method that assumes dynamic and measurement models are linear with Gaussian noise. However, for cellular images sequences, there are many interfering factors, e.g. background clutter and it is difficult to produce a clean image for cellular boundaries, which often causes the collapse of Kalman filter tracking.
Particle filters which is a form of a Sequential Monte Carlo method is sophisticated model estimation techniques based on random sampling. The particle filter does not require the assumption of a Gaussian distribution and the dynamic and measurement models may also be nonlinear. Particle Filters suffer from none of the disadvantages of the Kalman Filter. However there is a penalty to be paid in loss of speed. However, that being said, particle filters are not slow. In terms of speed, using a Particle Filter instead of a Kalman Filter is equivalent to changing from going extremely fast to being just fast enough for most applications.
The fundamental difference between particle filters and Kalman Filters is in how they represent state. The Kalman Filter stores one value for each of the parameters and stores the corresponding variances and correlations between each parameter in the form of a covariance matrix. Particle Filters maintain a collection of “particles”, each corresponding to a pair comprising the state vector and its corresponding weight. The overall behavior of the object being tracked can be derived from the statistics of the collection of particles. A single object may be tracked using dozens, hundreds or even thousands of particles. Particle filters require that a cell has a particular shape in order to keep track of it, but a cell is not an object that has one particular shape so it is sometimes difficult to track the cell.
Therefore, there is a need for a system and method for the automated tracking of cells that will be able to produce results that would depict a true image of cell movements in order to study cell function.